Overview

Dataset statistics

Number of variables37
Number of observations271798
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.7 MiB
Average record size in memory296.0 B

Variable types

Categorical18
Numeric19

Alerts

Jumps has constant value "0"Constant
JumpFlightTime has constant value "0"Constant
Time has a high cardinality: 271798 distinct valuesHigh cardinality
AvForceDevRate has a high cardinality: 291 distinct valuesHigh cardinality
AvStepImpulse has a high cardinality: 293 distinct valuesHigh cardinality
AvStepPeriod has a high cardinality: 453 distinct valuesHigh cardinality
Hour is highly overall correlated with Date and 4 other fieldsHigh correlation
HR is highly overall correlated with Activity and 5 other fieldsHigh correlation
Posture is highly overall correlated with Date and 4 other fieldsHigh correlation
Activity is highly overall correlated with HR and 2 other fieldsHigh correlation
PeakAcceleration is highly overall correlated with HR and 2 other fieldsHigh correlation
ECGNoise is highly overall correlated with HR and 3 other fieldsHigh correlation
CoreTemp is highly overall correlated with HR and 3 other fieldsHigh correlation
ImpulseLoad is highly overall correlated with WalkSteps and 2 other fieldsHigh correlation
WalkSteps is highly overall correlated with ImpulseLoad and 2 other fieldsHigh correlation
RunSteps is highly overall correlated with ImpulseLoad and 2 other fieldsHigh correlation
Bounds is highly overall correlated with ImpulseLoad and 4 other fieldsHigh correlation
MinorImpacts is highly overall correlated with Date and 5 other fieldsHigh correlation
PeakAccelPhi is highly overall correlated with HR and 4 other fieldsHigh correlation
Year is highly overall correlated with Month and 4 other fieldsHigh correlation
Month is highly overall correlated with Year and 4 other fieldsHigh correlation
Weekday is highly overall correlated with Year and 4 other fieldsHigh correlation
Date is highly overall correlated with Hour and 12 other fieldsHigh correlation
MajorImpacts is highly overall correlated with Bounds and 3 other fieldsHigh correlation
Activities is highly overall correlated with Hour and 14 other fieldsHigh correlation
Activities Detailed is highly overall correlated with Hour and 11 other fieldsHigh correlation
Controled stress is highly overall correlated with Hour and 3 other fieldsHigh correlation
stress is highly overall correlated with Month and 3 other fieldsHigh correlation
After controlled stress is highly overall correlated with Hour and 9 other fieldsHigh correlation
Name of the volunteer is highly overall correlated with Posture and 5 other fieldsHigh correlation
MajorImpacts is highly imbalanced (87.0%)Imbalance
AvForceDevRate is highly imbalanced (85.5%)Imbalance
AvStepImpulse is highly imbalanced (83.9%)Imbalance
AvStepPeriod is highly imbalanced (83.8%)Imbalance
Before Controled stress is highly imbalanced (95.9%)Imbalance
Name of the volunteer is highly imbalanced (88.4%)Imbalance
Time is uniformly distributedUniform
Time has unique valuesUnique
Hour has 7359 (2.7%) zerosZeros
RunSteps has 24746 (9.1%) zerosZeros
Bounds has 205517 (75.6%) zerosZeros
MinorImpacts has 177592 (65.3%) zerosZeros
peakAccelTheta has 3410 (1.3%) zerosZeros

Reproduction

Analysis started2023-07-16 14:51:49.537517
Analysis finished2023-07-16 14:52:57.339012
Duration1 minute and 7.8 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Time
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct271798
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
26.3.2020 9:02:28
 
1
2.4.2020 7:52:29
 
1
2.4.2020 7:52:15
 
1
2.4.2020 7:52:16
 
1
2.4.2020 7:52:17
 
1
Other values (271793)
271793 

Length

Max length18
Median length18
Mean length17.311062
Min length16

Characters and Unicode

Total characters4705112
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271798 ?
Unique (%)100.0%

Sample

1st row26.3.2020 9:02:28
2nd row26.3.2020 9:02:29
3rd row26.3.2020 9:02:30
4th row26.3.2020 9:02:31
5th row26.3.2020 9:02:32

Common Values

ValueCountFrequency (%)
26.3.2020 9:02:28 1
 
< 0.1%
2.4.2020 7:52:29 1
 
< 0.1%
2.4.2020 7:52:15 1
 
< 0.1%
2.4.2020 7:52:16 1
 
< 0.1%
2.4.2020 7:52:17 1
 
< 0.1%
2.4.2020 7:52:18 1
 
< 0.1%
2.4.2020 7:52:19 1
 
< 0.1%
2.4.2020 7:52:20 1
 
< 0.1%
2.4.2020 7:52:21 1
 
< 0.1%
2.4.2020 7:52:22 1
 
< 0.1%
Other values (271788) 271788
> 99.9%

Length

2023-07-16T17:52:57.437182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
26.3.2020 46122
 
8.5%
2.4.2020 30580
 
5.6%
29.3.2020 28733
 
5.3%
5.4.2020 24370
 
4.5%
27.3.2020 23729
 
4.4%
1.4.2022 20659
 
3.8%
19.5.2022 16149
 
3.0%
3.6.2022 10673
 
2.0%
3.4.2020 10129
 
1.9%
22.3.2020 8737
 
1.6%
Other values (85556) 323715
59.6%

Most occurring characters

ValueCountFrequency (%)
2 1010025
21.5%
0 646837
13.7%
. 543596
11.6%
: 543596
11.6%
1 412033
8.8%
3 318117
 
6.8%
4 281288
 
6.0%
271798
 
5.8%
5 218493
 
4.6%
6 157030
 
3.3%
Other values (3) 302299
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3346122
71.1%
Other Punctuation 1087192
 
23.1%
Space Separator 271798
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1010025
30.2%
0 646837
19.3%
1 412033
12.3%
3 318117
 
9.5%
4 281288
 
8.4%
5 218493
 
6.5%
6 157030
 
4.7%
9 125138
 
3.7%
7 101690
 
3.0%
8 75471
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 543596
50.0%
: 543596
50.0%
Space Separator
ValueCountFrequency (%)
271798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4705112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1010025
21.5%
0 646837
13.7%
. 543596
11.6%
: 543596
11.6%
1 412033
8.8%
3 318117
 
6.8%
4 281288
 
6.0%
271798
 
5.8%
5 218493
 
4.6%
6 157030
 
3.3%
Other values (3) 302299
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4705112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1010025
21.5%
0 646837
13.7%
. 543596
11.6%
: 543596
11.6%
1 412033
8.8%
3 318117
 
6.8%
4 281288
 
6.0%
271798
 
5.8%
5 218493
 
4.6%
6 157030
 
3.3%
Other values (3) 302299
 
6.4%

Year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2020
188667 
2022
79295 
2021
 
3836

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1087192
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 188667
69.4%
2022 79295
29.2%
2021 3836
 
1.4%

Length

2023-07-16T17:52:57.539493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:52:57.622184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 188667
69.4%
2022 79295
29.2%
2021 3836
 
1.4%

Most occurring characters

ValueCountFrequency (%)
2 622891
57.3%
0 460465
42.4%
1 3836
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1087192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 622891
57.3%
0 460465
42.4%
1 3836
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1087192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 622891
57.3%
0 460465
42.4%
1 3836
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1087192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 622891
57.3%
0 460465
42.4%
1 3836
 
0.4%

Month
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
March
117937 
April
99674 
May
24400 
June
20020 
January
 
9767

Length

Max length7
Median length5
Mean length4.8186668
Min length3

Characters and Unicode

Total characters1309704
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarch
2nd rowMarch
3rd rowMarch
4th rowMarch
5th rowMarch

Common Values

ValueCountFrequency (%)
March 117937
43.4%
April 99674
36.7%
May 24400
 
9.0%
June 20020
 
7.4%
January 9767
 
3.6%

Length

2023-07-16T17:52:57.719490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:52:57.814355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
march 117937
43.4%
april 99674
36.7%
may 24400
 
9.0%
june 20020
 
7.4%
january 9767
 
3.6%

Most occurring characters

ValueCountFrequency (%)
r 227378
17.4%
a 161871
12.4%
M 142337
10.9%
c 117937
9.0%
h 117937
9.0%
A 99674
7.6%
p 99674
7.6%
i 99674
7.6%
l 99674
7.6%
y 34167
 
2.6%
Other values (4) 109381
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1037906
79.2%
Uppercase Letter 271798
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 227378
21.9%
a 161871
15.6%
c 117937
11.4%
h 117937
11.4%
p 99674
9.6%
i 99674
9.6%
l 99674
9.6%
y 34167
 
3.3%
u 29787
 
2.9%
n 29787
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
M 142337
52.4%
A 99674
36.7%
J 29787
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1309704
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 227378
17.4%
a 161871
12.4%
M 142337
10.9%
c 117937
9.0%
h 117937
9.0%
A 99674
7.6%
p 99674
7.6%
i 99674
7.6%
l 99674
7.6%
y 34167
 
2.6%
Other values (4) 109381
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1309704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 227378
17.4%
a 161871
12.4%
M 142337
10.9%
c 117937
9.0%
h 117937
9.0%
A 99674
7.6%
p 99674
7.6%
i 99674
7.6%
l 99674
7.6%
y 34167
 
2.6%
Other values (4) 109381
8.4%

Weekday
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Thursday
92851 
Friday
67722 
Sunday
61840 
Saturday
17251 
Wednesday
12426 
Other values (2)
19708 

Length

Max length9
Median length6
Mean length6.9871191
Min length6

Characters and Unicode

Total characters1899085
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Thursday 92851
34.2%
Friday 67722
24.9%
Sunday 61840
22.8%
Saturday 17251
 
6.3%
Wednesday 12426
 
4.6%
Tuesday 10815
 
4.0%
Monday 8893
 
3.3%

Length

2023-07-16T17:52:57.922151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:52:58.017161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thursday 92851
34.2%
friday 67722
24.9%
sunday 61840
22.8%
saturday 17251
 
6.3%
wednesday 12426
 
4.6%
tuesday 10815
 
4.0%
monday 8893
 
3.3%

Most occurring characters

ValueCountFrequency (%)
a 289049
15.2%
d 284224
15.0%
y 271798
14.3%
u 182757
9.6%
r 177824
9.4%
s 116092
6.1%
T 103666
 
5.5%
h 92851
 
4.9%
n 83159
 
4.4%
S 79091
 
4.2%
Other values (7) 218574
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1627287
85.7%
Uppercase Letter 271798
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 289049
17.8%
d 284224
17.5%
y 271798
16.7%
u 182757
11.2%
r 177824
10.9%
s 116092
7.1%
h 92851
 
5.7%
n 83159
 
5.1%
i 67722
 
4.2%
e 35667
 
2.2%
Other values (2) 26144
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
T 103666
38.1%
S 79091
29.1%
F 67722
24.9%
W 12426
 
4.6%
M 8893
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1899085
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 289049
15.2%
d 284224
15.0%
y 271798
14.3%
u 182757
9.6%
r 177824
9.4%
s 116092
6.1%
T 103666
 
5.5%
h 92851
 
4.9%
n 83159
 
4.4%
S 79091
 
4.2%
Other values (7) 218574
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1899085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 289049
15.2%
d 284224
15.0%
y 271798
14.3%
u 182757
9.6%
r 177824
9.4%
s 116092
6.1%
T 103666
 
5.5%
h 92851
 
4.9%
n 83159
 
4.4%
S 79091
 
4.2%
Other values (7) 218574
11.5%

Hour
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.336754
Minimum0
Maximum23
Zeros7359
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:52:58.120130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median13
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.3384018
Coefficient of variation (CV)0.51378198
Kurtosis-0.96197307
Mean12.336754
Median Absolute Deviation (MAD)5
Skewness-0.32971125
Sum3353105
Variance40.175338
MonotonicityNot monotonic
2023-07-16T17:52:58.219971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
14 18574
 
6.8%
20 17699
 
6.5%
17 16829
 
6.2%
16 16632
 
6.1%
11 16310
 
6.0%
21 16229
 
6.0%
15 16198
 
6.0%
13 14489
 
5.3%
19 14101
 
5.2%
10 13938
 
5.1%
Other values (14) 110799
40.8%
ValueCountFrequency (%)
0 7359
2.7%
1 8825
3.2%
2 10319
3.8%
3 9198
3.4%
4 10800
4.0%
5 8288
3.0%
6 7557
2.8%
7 6895
2.5%
8 4335
1.6%
9 8261
3.0%
ValueCountFrequency (%)
23 3916
 
1.4%
22 4910
 
1.8%
21 16229
6.0%
20 17699
6.5%
19 14101
5.2%
18 7331
 
2.7%
17 16829
6.2%
16 16632
6.1%
15 16198
6.0%
14 18574
6.8%

HR
Real number (ℝ)

Distinct156
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.808439
Minimum35
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:52:58.327951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile56
Q169
median79
Q391
95-th percentile117
Maximum195
Range160
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.26343
Coefficient of variation (CV)0.22324628
Kurtosis1.2405303
Mean81.808439
Median Absolute Deviation (MAD)11
Skewness0.88546142
Sum22235370
Variance333.55287
MonotonicityNot monotonic
2023-07-16T17:52:58.449870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 7450
 
2.7%
79 7333
 
2.7%
78 7332
 
2.7%
76 7259
 
2.7%
75 7101
 
2.6%
80 7097
 
2.6%
74 7068
 
2.6%
81 6990
 
2.6%
82 6671
 
2.5%
73 6650
 
2.4%
Other values (146) 200847
73.9%
ValueCountFrequency (%)
35 4
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
39 1
 
< 0.1%
43 1
 
< 0.1%
44 6
 
< 0.1%
45 18
< 0.1%
46 29
< 0.1%
47 27
< 0.1%
ValueCountFrequency (%)
195 1
 
< 0.1%
194 5
 
< 0.1%
193 2
 
< 0.1%
191 1
 
< 0.1%
189 5
 
< 0.1%
188 8
 
< 0.1%
187 5
 
< 0.1%
186 5
 
< 0.1%
185 9
< 0.1%
184 22
< 0.1%

BR
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.709733
Minimum4
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:52:58.555262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q114
median17
Q319
95-th percentile23
Maximum32
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6975016
Coefficient of variation (CV)0.22127832
Kurtosis-0.13302778
Mean16.709733
Median Absolute Deviation (MAD)2
Skewness0.0017862263
Sum4541672
Variance13.671518
MonotonicityNot monotonic
2023-07-16T17:52:58.650751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
17 38761
14.3%
16 32949
12.1%
18 25636
9.4%
15 20720
7.6%
19 20385
 
7.5%
14 18329
 
6.7%
20 17385
 
6.4%
13 16690
 
6.1%
21 15357
 
5.7%
12 14397
 
5.3%
Other values (19) 51189
18.8%
ValueCountFrequency (%)
4 34
 
< 0.1%
5 44
 
< 0.1%
6 294
 
0.1%
7 647
 
0.2%
8 2290
 
0.8%
9 3687
 
1.4%
10 6024
 
2.2%
11 10194
3.8%
12 14397
5.3%
13 16690
6.1%
ValueCountFrequency (%)
32 2
 
< 0.1%
31 26
 
< 0.1%
30 42
 
< 0.1%
29 58
 
< 0.1%
28 172
 
0.1%
27 399
 
0.1%
26 1130
 
0.4%
25 2582
 
0.9%
24 4532
1.7%
23 8122
3.0%

Posture
Real number (ℝ)

Distinct233
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.3279494
Minimum-179
Maximum179
Zeros267
Zeros (%)0.1%
Negative159689
Negative (%)58.8%
Memory size2.1 MiB
2023-07-16T17:52:58.869282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-179
5-th percentile-83
Q1-24
median-6
Q311
95-th percentile76
Maximum179
Range358
Interquartile range (IQR)35

Descriptive statistics

Standard deviation45.84024
Coefficient of variation (CV)-4.9142891
Kurtosis2.5894834
Mean-9.3279494
Median Absolute Deviation (MAD)18
Skewness0.60647163
Sum-2535318
Variance2101.3276
MonotonicityNot monotonic
2023-07-16T17:52:59.001282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7 9435
 
3.5%
-78 9379
 
3.5%
10 9036
 
3.3%
-8 8948
 
3.3%
-10 8181
 
3.0%
12 8000
 
2.9%
-79 7545
 
2.8%
11 7347
 
2.7%
-4 6853
 
2.5%
8 5995
 
2.2%
Other values (223) 191079
70.3%
ValueCountFrequency (%)
-179 17
 
< 0.1%
-178 4
 
< 0.1%
-177 24
 
< 0.1%
-176 22
 
< 0.1%
-175 15
 
< 0.1%
-174 2
 
< 0.1%
-173 5
 
< 0.1%
-172 40
< 0.1%
-170 2
 
< 0.1%
-169 70
< 0.1%
ValueCountFrequency (%)
179 368
0.1%
178 346
0.1%
177 667
0.2%
176 268
0.1%
175 295
0.1%
174 331
0.1%
173 103
 
< 0.1%
172 182
 
0.1%
171 124
 
< 0.1%
170 195
 
0.1%

Activity
Real number (ℝ)

Distinct107
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046164762
Minimum0
Maximum1.32
Zeros490
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:52:59.130283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.01
median0.01
Q30.04
95-th percentile0.2
Maximum1.32
Range1.32
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.072492419
Coefficient of variation (CV)1.5702977
Kurtosis18.121322
Mean0.046164762
Median Absolute Deviation (MAD)0
Skewness3.4382067
Sum12547.49
Variance0.0052551508
MonotonicityNot monotonic
2023-07-16T17:52:59.259280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 136601
50.3%
0.02 44964
 
16.5%
0.03 13040
 
4.8%
0.04 9293
 
3.4%
0.05 7235
 
2.7%
0.06 5933
 
2.2%
0.07 5100
 
1.9%
0.08 4475
 
1.6%
0.09 3764
 
1.4%
0.1 3434
 
1.3%
Other values (97) 37959
 
14.0%
ValueCountFrequency (%)
0 490
 
0.2%
0.01 136601
50.3%
0.02 44964
 
16.5%
0.03 13040
 
4.8%
0.04 9293
 
3.4%
0.05 7235
 
2.7%
0.06 5933
 
2.2%
0.07 5100
 
1.9%
0.08 4475
 
1.6%
0.09 3764
 
1.4%
ValueCountFrequency (%)
1.32 1
 
< 0.1%
1.17 2
< 0.1%
1.13 1
 
< 0.1%
1.11 1
 
< 0.1%
1.1 3
< 0.1%
1.09 1
 
< 0.1%
1.06 1
 
< 0.1%
1.02 4
< 0.1%
1 1
 
< 0.1%
0.99 1
 
< 0.1%

PeakAcceleration
Real number (ℝ)

Distinct173
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10416806
Minimum0.01
Maximum6.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:52:59.375280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.03
median0.04
Q30.12
95-th percentile0.39
Maximum6.21
Range6.2
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.13722855
Coefficient of variation (CV)1.3173765
Kurtosis38.190439
Mean0.10416806
Median Absolute Deviation (MAD)0.01
Skewness3.902143
Sum28312.67
Variance0.018831675
MonotonicityNot monotonic
2023-07-16T17:52:59.491281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 79939
29.4%
0.04 58986
21.7%
0.06 21583
 
7.9%
0.12 9133
 
3.4%
0.07 8734
 
3.2%
0.1 8385
 
3.1%
0.02 7711
 
2.8%
0.08 7614
 
2.8%
0.09 6826
 
2.5%
0.13 6335
 
2.3%
Other values (163) 56552
20.8%
ValueCountFrequency (%)
0.01 13
 
< 0.1%
0.02 7711
 
2.8%
0.03 79939
29.4%
0.04 58986
21.7%
0.06 21583
 
7.9%
0.07 8734
 
3.2%
0.08 7614
 
2.8%
0.09 6826
 
2.5%
0.1 8385
 
3.1%
0.12 9133
 
3.4%
ValueCountFrequency (%)
6.21 1
< 0.1%
3.69 1
< 0.1%
3.63 1
< 0.1%
3.06 1
< 0.1%
2.95 1
< 0.1%
2.85 1
< 0.1%
2.66 1
< 0.1%
2.59 1
< 0.1%
2.54 2
< 0.1%
2.53 1
< 0.1%

BRAmplitude
Real number (ℝ)

Distinct24489
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5664.8311
Minimum305
Maximum65348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:52:59.605281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum305
5-th percentile1279
Q12296
median3923
Q36848
95-th percentile15320
Maximum65348
Range65043
Interquartile range (IQR)4552

Descriptive statistics

Standard deviation5891.0561
Coefficient of variation (CV)1.039935
Kurtosis22.525687
Mean5664.8311
Median Absolute Deviation (MAD)1904
Skewness3.86996
Sum1.5396898 × 109
Variance34704542
MonotonicityNot monotonic
2023-07-16T17:52:59.730280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1873 84
 
< 0.1%
2195 83
 
< 0.1%
2125 80
 
< 0.1%
2411 80
 
< 0.1%
2001 79
 
< 0.1%
2342 79
 
< 0.1%
2071 79
 
< 0.1%
2382 78
 
< 0.1%
2205 78
 
< 0.1%
2186 77
 
< 0.1%
Other values (24479) 271001
99.7%
ValueCountFrequency (%)
305 1
< 0.1%
307 1
< 0.1%
315 1
< 0.1%
329 1
< 0.1%
340 1
< 0.1%
345 1
< 0.1%
346 1
< 0.1%
348 2
< 0.1%
351 1
< 0.1%
356 1
< 0.1%
ValueCountFrequency (%)
65348 1
< 0.1%
65285 1
< 0.1%
65199 1
< 0.1%
65084 1
< 0.1%
65063 1
< 0.1%
64929 1
< 0.1%
64901 1
< 0.1%
64821 1
< 0.1%
64722 1
< 0.1%
64684 1
< 0.1%

ECGAmplitude
Real number (ℝ)

Distinct269
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0031274249
Minimum0
Maximum0.01698
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:52:59.845280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00104
Q10.00264
median0.0033
Q30.00374
95-th percentile0.00436
Maximum0.01698
Range0.01698
Interquartile range (IQR)0.0011

Descriptive statistics

Standard deviation0.00091390068
Coefficient of variation (CV)0.29222146
Kurtosis0.89025755
Mean0.0031274249
Median Absolute Deviation (MAD)0.00054
Skewness-0.81650339
Sum850.02784
Variance8.3521445 × 10-7
MonotonicityNot monotonic
2023-07-16T17:52:59.973280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00362 3420
 
1.3%
0.00358 3405
 
1.3%
0.00366 3398
 
1.3%
0.00368 3328
 
1.2%
0.0037 3316
 
1.2%
0.00364 3288
 
1.2%
0.00354 3284
 
1.2%
0.00374 3245
 
1.2%
0.00352 3230
 
1.2%
0.0036 3130
 
1.2%
Other values (259) 238754
87.8%
ValueCountFrequency (%)
0 7
 
< 0.1%
0.00016 4
 
< 0.1%
0.00018 9
 
< 0.1%
0.0002 12
< 0.1%
0.00022 4
 
< 0.1%
0.00024 24
< 0.1%
0.00026 7
 
< 0.1%
0.00028 14
< 0.1%
0.0003 11
< 0.1%
0.00032 20
< 0.1%
ValueCountFrequency (%)
0.01698 2
 
< 0.1%
0.00846 2
 
< 0.1%
0.00838 1
 
< 0.1%
0.00546 2
 
< 0.1%
0.00542 2
 
< 0.1%
0.0054 4
 
< 0.1%
0.00538 4
 
< 0.1%
0.00536 8
< 0.1%
0.00534 4
 
< 0.1%
0.00532 16
< 0.1%

ECGNoise
Real number (ℝ)

Distinct345
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00019024415
Minimum2 × 10-5
Maximum0.01222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:00.102281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2 × 10-5
5-th percentile6 × 10-5
Q10.0001
median0.00012
Q30.00018
95-th percentile0.00054
Maximum0.01222
Range0.0122
Interquartile range (IQR)8 × 10-5

Descriptive statistics

Standard deviation0.00028826701
Coefficient of variation (CV)1.5152477
Kurtosis266.30131
Mean0.00019024415
Median Absolute Deviation (MAD)4 × 10-5
Skewness12.269971
Sum51.70798
Variance8.3097869 × 10-8
MonotonicityNot monotonic
2023-07-16T17:53:00.231280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0001 41716
15.3%
0.00012 40017
14.7%
8 × 10-531823
11.7%
0.00014 30093
11.1%
6 × 10-522188
8.2%
0.00016 20714
7.6%
0.00018 14765
 
5.4%
0.0002 10174
 
3.7%
4 × 10-57876
 
2.9%
0.00022 6738
 
2.5%
Other values (335) 45694
16.8%
ValueCountFrequency (%)
2 × 10-575
 
< 0.1%
4 × 10-57876
 
2.9%
6 × 10-522188
8.2%
8 × 10-531823
11.7%
0.0001 41716
15.3%
0.00012 40017
14.7%
0.00014 30093
11.1%
0.00016 20714
7.6%
0.00018 14765
 
5.4%
0.0002 10174
 
3.7%
ValueCountFrequency (%)
0.01222 1
< 0.1%
0.0117 1
< 0.1%
0.0116 1
< 0.1%
0.01152 1
< 0.1%
0.01026 1
< 0.1%
0.01018 1
< 0.1%
0.01016 1
< 0.1%
0.01006 1
< 0.1%
0.01002 1
< 0.1%
0.00986 1
< 0.1%

HRConfidence
Real number (ℝ)

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.085674
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:00.353280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile82
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.558287
Coefficient of variation (CV)0.11905245
Kurtosis32.243972
Mean97.085674
Median Absolute Deviation (MAD)0
Skewness-5.4091181
Sum26387692
Variance133.59401
MonotonicityNot monotonic
2023-07-16T17:53:00.477281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 230643
84.9%
99 6444
 
2.4%
98 3468
 
1.3%
97 2587
 
1.0%
96 1949
 
0.7%
95 1579
 
0.6%
94 1417
 
0.5%
93 1290
 
0.5%
92 1211
 
0.4%
91 1160
 
0.4%
Other values (90) 20050
 
7.4%
ValueCountFrequency (%)
1 157
0.1%
2 136
0.1%
3 120
< 0.1%
4 90
< 0.1%
5 106
< 0.1%
6 101
< 0.1%
7 107
< 0.1%
8 111
< 0.1%
9 110
< 0.1%
10 89
< 0.1%
ValueCountFrequency (%)
100 230643
84.9%
99 6444
 
2.4%
98 3468
 
1.3%
97 2587
 
1.0%
96 1949
 
0.7%
95 1579
 
0.6%
94 1417
 
0.5%
93 1290
 
0.5%
92 1211
 
0.4%
91 1160
 
0.4%

HRV
Real number (ℝ)

Distinct229
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.191668
Minimum8
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:00.593294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile23
Q134
median47
Q372
95-th percentile148
Maximum236
Range228
Interquartile range (IQR)38

Descriptive statistics

Standard deviation38.946443
Coefficient of variation (CV)0.64704044
Kurtosis2.4048006
Mean60.191668
Median Absolute Deviation (MAD)16
Skewness1.6293171
Sum16359975
Variance1516.8255
MonotonicityNot monotonic
2023-07-16T17:53:00.725281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 6352
 
2.3%
32 6307
 
2.3%
36 6134
 
2.3%
34 5838
 
2.1%
38 5756
 
2.1%
39 5730
 
2.1%
33 5648
 
2.1%
37 5605
 
2.1%
30 5566
 
2.0%
31 5558
 
2.0%
Other values (219) 213304
78.5%
ValueCountFrequency (%)
8 12
 
< 0.1%
9 46
 
< 0.1%
10 176
 
0.1%
11 217
 
0.1%
12 269
 
0.1%
13 549
0.2%
14 435
0.2%
15 679
0.2%
16 598
0.2%
17 872
0.3%
ValueCountFrequency (%)
236 4
 
< 0.1%
235 6
 
< 0.1%
234 4
 
< 0.1%
233 3
 
< 0.1%
232 10
 
< 0.1%
231 9
 
< 0.1%
230 7
 
< 0.1%
229 3
 
< 0.1%
228 5
 
< 0.1%
227 27
< 0.1%

CoreTemp
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.06984
Minimum36.5
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:00.824294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.5
5-th percentile36.6
Q136.9
median37.1
Q337.2
95-th percentile37.7
Maximum38
Range1.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.30359826
Coefficient of variation (CV)0.0081898994
Kurtosis0.63969153
Mean37.06984
Median Absolute Deviation (MAD)0.1
Skewness0.38890533
Sum10075508
Variance0.092171905
MonotonicityNot monotonic
2023-07-16T17:53:00.925311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
37.1 66276
24.4%
37 43454
16.0%
37.2 33473
12.3%
36.6 23464
 
8.6%
37.3 20748
 
7.6%
36.9 20633
 
7.6%
36.7 14799
 
5.4%
37.4 11117
 
4.1%
36.5 10412
 
3.8%
37.5 5878
 
2.2%
Other values (6) 21544
 
7.9%
ValueCountFrequency (%)
36.5 10412
 
3.8%
36.6 23464
 
8.6%
36.7 14799
 
5.4%
36.8 3872
 
1.4%
36.9 20633
 
7.6%
37 43454
16.0%
37.1 66276
24.4%
37.2 33473
12.3%
37.3 20748
 
7.6%
37.4 11117
 
4.1%
ValueCountFrequency (%)
38 1020
 
0.4%
37.9 5222
 
1.9%
37.8 5101
 
1.9%
37.7 3210
 
1.2%
37.6 3119
 
1.1%
37.5 5878
 
2.2%
37.4 11117
 
4.1%
37.3 20748
 
7.6%
37.2 33473
12.3%
37.1 66276
24.4%

ImpulseLoad
Real number (ℝ)

Distinct12043
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3334.0444
Minimum12
Maximum20230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:01.038007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile127
Q1483
median978
Q32616
95-th percentile15418
Maximum20230
Range20218
Interquartile range (IQR)2133

Descriptive statistics

Standard deviation5043.4362
Coefficient of variation (CV)1.5127082
Kurtosis1.7119401
Mean3334.0444
Median Absolute Deviation (MAD)686
Skewness1.783043
Sum9.0618661 × 108
Variance25436249
MonotonicityNot monotonic
2023-07-16T17:53:01.164644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 4424
 
1.6%
892 3516
 
1.3%
609 3076
 
1.1%
507 2847
 
1.0%
592 2837
 
1.0%
743 2651
 
1.0%
163 2484
 
0.9%
258 2389
 
0.9%
362 2263
 
0.8%
3530 2196
 
0.8%
Other values (12033) 243115
89.4%
ValueCountFrequency (%)
12 42
 
< 0.1%
14 13
 
< 0.1%
22 1
 
< 0.1%
24 4
 
< 0.1%
26 2
 
< 0.1%
29 15
 
< 0.1%
32 8
 
< 0.1%
33 5
 
< 0.1%
34 121
 
< 0.1%
35 454
0.2%
ValueCountFrequency (%)
20230 4
< 0.1%
20227 2
 
< 0.1%
20225 4
< 0.1%
20220 3
 
< 0.1%
20218 1
 
< 0.1%
20216 4
< 0.1%
20214 1
 
< 0.1%
20212 1
 
< 0.1%
20210 8
< 0.1%
20208 1
 
< 0.1%

WalkSteps
Real number (ℝ)

Distinct7160
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.8602
Minimum3
Maximum7586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:01.398094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile27
Q1152
median237
Q3935
95-th percentile5621
Maximum7586
Range7583
Interquartile range (IQR)783

Descriptive statistics

Standard deviation1870.9751
Coefficient of variation (CV)1.5658527
Kurtosis1.6980625
Mean1194.8602
Median Absolute Deviation (MAD)182
Skewness1.7695717
Sum3.2476061 × 108
Variance3500547.7
MonotonicityNot monotonic
2023-07-16T17:53:01.512098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187 13410
 
4.9%
209 13202
 
4.9%
171 7017
 
2.6%
173 5831
 
2.1%
170 4772
 
1.8%
24 4723
 
1.7%
210 4651
 
1.7%
46 4359
 
1.6%
17 2799
 
1.0%
125 2670
 
1.0%
Other values (7150) 208364
76.7%
ValueCountFrequency (%)
3 42
 
< 0.1%
4 13
 
< 0.1%
6 1
 
< 0.1%
7 67
 
< 0.1%
8 142
 
0.1%
9 121
 
< 0.1%
10 760
0.3%
11 427
0.2%
12 982
0.4%
13 135
 
< 0.1%
ValueCountFrequency (%)
7586 3
< 0.1%
7585 1
 
< 0.1%
7584 1
 
< 0.1%
7582 1
 
< 0.1%
7581 1
 
< 0.1%
7580 5
< 0.1%
7579 6
< 0.1%
7577 1
 
< 0.1%
7575 1
 
< 0.1%
7573 1
 
< 0.1%

RunSteps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct116
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.659611
Minimum0
Maximum153
Zeros24746
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:01.630097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile61
Maximum153
Range153
Interquartile range (IQR)5

Descriptive statistics

Standard deviation27.11677
Coefficient of variation (CV)1.9851787
Kurtosis8.4844935
Mean13.659611
Median Absolute Deviation (MAD)2
Skewness2.8845036
Sum3712655
Variance735.3192
MonotonicityNot monotonic
2023-07-16T17:53:01.757098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 72713
26.8%
1 48010
17.7%
2 31674
11.7%
0 24746
 
9.1%
6 15875
 
5.8%
7 11530
 
4.2%
4 9530
 
3.5%
125 7619
 
2.8%
38 5067
 
1.9%
60 4856
 
1.8%
Other values (106) 40178
14.8%
ValueCountFrequency (%)
0 24746
 
9.1%
1 48010
17.7%
2 31674
11.7%
3 72713
26.8%
4 9530
 
3.5%
5 2792
 
1.0%
6 15875
 
5.8%
7 11530
 
4.2%
8 1214
 
0.4%
9 3
 
< 0.1%
ValueCountFrequency (%)
153 4
 
< 0.1%
152 871
0.3%
151 6
 
< 0.1%
150 1
 
< 0.1%
148 2
 
< 0.1%
147 1
 
< 0.1%
145 1
 
< 0.1%
142 1
 
< 0.1%
141 2
 
< 0.1%
139 1
 
< 0.1%

Bounds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3730712
Minimum0
Maximum16
Zeros205517
Zeros (%)75.6%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:01.862088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.7275688
Coefficient of variation (CV)2.7147674
Kurtosis7.8491497
Mean1.3730712
Median Absolute Deviation (MAD)0
Skewness2.9837884
Sum373198
Variance13.894769
MonotonicityNot monotonic
2023-07-16T17:53:01.960094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 205517
75.6%
1 36012
 
13.2%
16 10936
 
4.0%
8 8664
 
3.2%
10 7414
 
2.7%
2 1254
 
0.5%
11 799
 
0.3%
4 539
 
0.2%
7 521
 
0.2%
15 67
 
< 0.1%
Other values (6) 75
 
< 0.1%
ValueCountFrequency (%)
0 205517
75.6%
1 36012
 
13.2%
2 1254
 
0.5%
3 9
 
< 0.1%
4 539
 
0.2%
5 8
 
< 0.1%
6 9
 
< 0.1%
7 521
 
0.2%
8 8664
 
3.2%
9 23
 
< 0.1%
ValueCountFrequency (%)
16 10936
4.0%
15 67
 
< 0.1%
13 13
 
< 0.1%
12 13
 
< 0.1%
11 799
 
0.3%
10 7414
2.7%
9 23
 
< 0.1%
8 8664
3.2%
7 521
 
0.2%
6 9
 
< 0.1%

Jumps
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
271798 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters271798
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 271798
100.0%

Length

2023-07-16T17:53:02.063088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:02.142087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 271798
100.0%

Most occurring characters

ValueCountFrequency (%)
0 271798
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 271798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 271798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 271798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 271798
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 271798
100.0%

MinorImpacts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97470916
Minimum0
Maximum8
Zeros177592
Zeros (%)65.3%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:02.215096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5601898
Coefficient of variation (CV)1.6006722
Kurtosis0.81055779
Mean0.97470916
Median Absolute Deviation (MAD)0
Skewness1.4117078
Sum264924
Variance2.4341922
MonotonicityNot monotonic
2023-07-16T17:53:02.318095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 177592
65.3%
3 38096
 
14.0%
1 23239
 
8.6%
4 11304
 
4.2%
2 9651
 
3.6%
5 8644
 
3.2%
6 3253
 
1.2%
7 11
 
< 0.1%
8 8
 
< 0.1%
ValueCountFrequency (%)
0 177592
65.3%
1 23239
 
8.6%
2 9651
 
3.6%
3 38096
 
14.0%
4 11304
 
4.2%
5 8644
 
3.2%
6 3253
 
1.2%
7 11
 
< 0.1%
8 8
 
< 0.1%
ValueCountFrequency (%)
8 8
 
< 0.1%
7 11
 
< 0.1%
6 3253
 
1.2%
5 8644
 
3.2%
4 11304
 
4.2%
3 38096
 
14.0%
2 9651
 
3.6%
1 23239
 
8.6%
0 177592
65.3%

Date
Categorical

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
26.3.2020
46122 
2.4.2020
30580 
29.3.2020
28733 
5.4.2020
24370 
27.3.2020
23729 
Other values (19)
118264 

Length

Max length9
Median length9
Mean length8.6121568
Min length8

Characters and Unicode

Total characters2340767
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26.3.2020
2nd row26.3.2020
3rd row26.3.2020
4th row26.3.2020
5th row26.3.2020

Common Values

ValueCountFrequency (%)
26.3.2020 46122
17.0%
2.4.2020 30580
11.3%
29.3.2020 28733
10.6%
5.4.2020 24370
9.0%
27.3.2020 23729
8.7%
1.4.2022 20659
 
7.6%
19.5.2022 16149
 
5.9%
3.6.2022 10673
 
3.9%
3.4.2020 10129
 
3.7%
22.3.2020 8737
 
3.2%
Other values (14) 51917
19.1%

Length

2023-07-16T17:53:02.421510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
26.3.2020 46122
17.0%
2.4.2020 30580
11.3%
29.3.2020 28733
10.6%
5.4.2020 24370
9.0%
27.3.2020 23729
8.7%
1.4.2022 20659
 
7.6%
19.5.2022 16149
 
5.9%
3.6.2022 10673
 
3.9%
3.4.2020 10129
 
3.7%
22.3.2020 8737
 
3.2%
Other values (14) 51917
19.1%

Most occurring characters

ValueCountFrequency (%)
2 793811
33.9%
. 543596
23.2%
0 462997
19.8%
3 144375
 
6.2%
4 108197
 
4.6%
6 78683
 
3.4%
1 78203
 
3.3%
5 48770
 
2.1%
9 48630
 
2.1%
7 23817
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1797171
76.8%
Other Punctuation 543596
 
23.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 793811
44.2%
0 462997
25.8%
3 144375
 
8.0%
4 108197
 
6.0%
6 78683
 
4.4%
1 78203
 
4.4%
5 48770
 
2.7%
9 48630
 
2.7%
7 23817
 
1.3%
8 9688
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 543596
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2340767
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 793811
33.9%
. 543596
23.2%
0 462997
19.8%
3 144375
 
6.2%
4 108197
 
4.6%
6 78683
 
3.4%
1 78203
 
3.3%
5 48770
 
2.1%
9 48630
 
2.1%
7 23817
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2340767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 793811
33.9%
. 543596
23.2%
0 462997
19.8%
3 144375
 
6.2%
4 108197
 
4.6%
6 78683
 
3.4%
1 78203
 
3.3%
5 48770
 
2.1%
9 48630
 
2.1%
7 23817
 
1.0%

MajorImpacts
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
263057 
2
 
8737
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters271798
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 263057
96.8%
2 8737
 
3.2%
1 4
 
< 0.1%

Length

2023-07-16T17:53:02.517454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:02.597453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 263057
96.8%
2 8737
 
3.2%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 263057
96.8%
2 8737
 
3.2%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 271798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 263057
96.8%
2 8737
 
3.2%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 271798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 263057
96.8%
2 8737
 
3.2%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 263057
96.8%
2 8737
 
3.2%
1 4
 
< 0.1%

AvForceDevRate
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct291
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
242125 
0,33
 
926
0,3
 
869
0,32
 
848
0,35
 
804
Other values (286)
26226 

Length

Max length4
Median length1
Mean length1.3164814
Min length1

Characters and Unicode

Total characters357817
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 242125
89.1%
0,33 926
 
0.3%
0,3 869
 
0.3%
0,32 848
 
0.3%
0,35 804
 
0.3%
0,4 786
 
0.3%
0,28 782
 
0.3%
0,25 737
 
0.3%
0,45 734
 
0.3%
0,36 725
 
0.3%
Other values (281) 22462
 
8.3%

Length

2023-07-16T17:53:02.688453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 242125
89.1%
0,33 926
 
0.3%
0,3 869
 
0.3%
0,32 848
 
0.3%
0,35 804
 
0.3%
0,4 786
 
0.3%
0,28 782
 
0.3%
0,25 737
 
0.3%
0,45 734
 
0.3%
0,36 725
 
0.3%
Other values (281) 22462
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 270935
75.7%
, 29634
 
8.3%
3 10575
 
3.0%
4 8975
 
2.5%
2 8817
 
2.5%
5 6876
 
1.9%
1 5703
 
1.6%
6 5023
 
1.4%
7 3990
 
1.1%
8 3781
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 328183
91.7%
Other Punctuation 29634
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 270935
82.6%
3 10575
 
3.2%
4 8975
 
2.7%
2 8817
 
2.7%
5 6876
 
2.1%
1 5703
 
1.7%
6 5023
 
1.5%
7 3990
 
1.2%
8 3781
 
1.2%
9 3508
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 29634
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 357817
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 270935
75.7%
, 29634
 
8.3%
3 10575
 
3.0%
4 8975
 
2.5%
2 8817
 
2.5%
5 6876
 
1.9%
1 5703
 
1.6%
6 5023
 
1.4%
7 3990
 
1.1%
8 3781
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357817
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 270935
75.7%
, 29634
 
8.3%
3 10575
 
3.0%
4 8975
 
2.5%
2 8817
 
2.5%
5 6876
 
1.9%
1 5703
 
1.6%
6 5023
 
1.4%
7 3990
 
1.1%
8 3781
 
1.1%

AvStepImpulse
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct293
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
242125 
2,44
 
406
2,43
 
404
2,4
 
398
2,48
 
383
Other values (288)
28082 

Length

Max length4
Median length1
Mean length1.3142113
Min length1

Characters and Unicode

Total characters357200
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 242125
89.1%
2,44 406
 
0.1%
2,43 404
 
0.1%
2,4 398
 
0.1%
2,48 383
 
0.1%
2,35 375
 
0.1%
2,29 369
 
0.1%
2,32 359
 
0.1%
2,58 339
 
0.1%
2,22 332
 
0.1%
Other values (283) 26308
 
9.7%

Length

2023-07-16T17:53:02.791178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 242125
89.1%
2,44 406
 
0.1%
2,43 404
 
0.1%
2,4 398
 
0.1%
2,48 383
 
0.1%
2,35 375
 
0.1%
2,29 369
 
0.1%
2,32 359
 
0.1%
2,58 339
 
0.1%
2,22 332
 
0.1%
Other values (283) 26308
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 244962
68.6%
, 29376
 
8.2%
2 26970
 
7.6%
1 11049
 
3.1%
3 9451
 
2.6%
4 6651
 
1.9%
9 6089
 
1.7%
6 5741
 
1.6%
8 5728
 
1.6%
7 5708
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327824
91.8%
Other Punctuation 29376
 
8.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244962
74.7%
2 26970
 
8.2%
1 11049
 
3.4%
3 9451
 
2.9%
4 6651
 
2.0%
9 6089
 
1.9%
6 5741
 
1.8%
8 5728
 
1.7%
7 5708
 
1.7%
5 5475
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 29376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 357200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 244962
68.6%
, 29376
 
8.2%
2 26970
 
7.6%
1 11049
 
3.1%
3 9451
 
2.6%
4 6651
 
1.9%
9 6089
 
1.7%
6 5741
 
1.6%
8 5728
 
1.6%
7 5708
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 244962
68.6%
, 29376
 
8.2%
2 26970
 
7.6%
1 11049
 
3.1%
3 9451
 
2.6%
4 6651
 
1.9%
9 6089
 
1.7%
6 5741
 
1.6%
8 5728
 
1.6%
7 5708
 
1.6%

AvStepPeriod
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct453
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
242125 
0,443
 
280
0,407
 
244
0,504
 
234
0,425
 
228
Other values (448)
28687 

Length

Max length5
Median length1
Mean length1.4260223
Min length1

Characters and Unicode

Total characters387590
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 242125
89.1%
0,443 280
 
0.1%
0,407 244
 
0.1%
0,504 234
 
0.1%
0,425 228
 
0.1%
0,419 226
 
0.1%
0,514 201
 
0.1%
0,548 200
 
0.1%
0,347 197
 
0.1%
0,526 192
 
0.1%
Other values (443) 27671
 
10.2%

Length

2023-07-16T17:53:02.904557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 242125
89.1%
0,443 280
 
0.1%
0,407 244
 
0.1%
0,504 234
 
0.1%
0,425 228
 
0.1%
0,419 226
 
0.1%
0,514 201
 
0.1%
0,548 200
 
0.1%
0,347 197
 
0.1%
0,526 192
 
0.1%
Other values (443) 27671
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 274341
70.8%
, 29673
 
7.7%
4 16179
 
4.2%
5 13894
 
3.6%
3 12845
 
3.3%
6 10396
 
2.7%
2 7050
 
1.8%
1 6282
 
1.6%
8 5867
 
1.5%
7 5655
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 357917
92.3%
Other Punctuation 29673
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274341
76.6%
4 16179
 
4.5%
5 13894
 
3.9%
3 12845
 
3.6%
6 10396
 
2.9%
2 7050
 
2.0%
1 6282
 
1.8%
8 5867
 
1.6%
7 5655
 
1.6%
9 5408
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 29673
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274341
70.8%
, 29673
 
7.7%
4 16179
 
4.2%
5 13894
 
3.6%
3 12845
 
3.3%
6 10396
 
2.7%
2 7050
 
1.8%
1 6282
 
1.6%
8 5867
 
1.5%
7 5655
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 274341
70.8%
, 29673
 
7.7%
4 16179
 
4.2%
5 13894
 
3.6%
3 12845
 
3.3%
6 10396
 
2.7%
2 7050
 
1.8%
1 6282
 
1.6%
8 5867
 
1.5%
7 5655
 
1.5%

JumpFlightTime
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
271798 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters271798
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 271798
100.0%

Length

2023-07-16T17:53:03.005550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:03.078533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 271798
100.0%

Most occurring characters

ValueCountFrequency (%)
0 271798
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 271798
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 271798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 271798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 271798
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 271798
100.0%

PeakAccelPhi
Real number (ℝ)

Distinct163
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.01945
Minimum0
Maximum180
Zeros35
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-07-16T17:53:03.165748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile94
Q1116
median162
Q3169
95-th percentile177
Maximum180
Range180
Interquartile range (IQR)53

Descriptive statistics

Standard deviation33.301126
Coefficient of variation (CV)0.22805952
Kurtosis2.623303
Mean146.01945
Median Absolute Deviation (MAD)10
Skewness-1.5287309
Sum39687794
Variance1108.965
MonotonicityNot monotonic
2023-07-16T17:53:03.282787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169 19047
 
7.0%
168 14450
 
5.3%
170 12165
 
4.5%
166 10770
 
4.0%
102 10427
 
3.8%
165 9194
 
3.4%
101 8856
 
3.3%
172 8289
 
3.0%
163 7103
 
2.6%
167 6688
 
2.5%
Other values (153) 164809
60.6%
ValueCountFrequency (%)
0 35
 
< 0.1%
1 190
0.1%
2 317
0.1%
3 374
0.1%
4 343
0.1%
5 195
0.1%
6 378
0.1%
7 269
0.1%
8 306
0.1%
9 111
 
< 0.1%
ValueCountFrequency (%)
180 339
 
0.1%
179 2806
 
1.0%
178 5320
2.0%
177 5237
1.9%
176 5059
1.9%
175 3075
 
1.1%
174 6610
2.4%
173 6160
2.3%
172 8289
3.0%
171 4562
1.7%

peakAccelTheta
Real number (ℝ)

Distinct360
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.331824
Minimum-179
Maximum180
Zeros3410
Zeros (%)1.3%
Negative138839
Negative (%)51.1%
Memory size2.1 MiB
2023-07-16T17:53:03.390428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-179
5-th percentile-155
Q1-63
median-6
Q3140
95-th percentile172
Maximum180
Range359
Interquartile range (IQR)203

Descriptive statistics

Standard deviation111.53177
Coefficient of variation (CV)5.4855762
Kurtosis-1.3943017
Mean20.331824
Median Absolute Deviation (MAD)115
Skewness-0.059385015
Sum5526149
Variance12439.335
MonotonicityNot monotonic
2023-07-16T17:53:03.503428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 5053
 
1.9%
-90 4086
 
1.5%
149 3825
 
1.4%
151 3764
 
1.4%
153 3661
 
1.3%
143 3645
 
1.3%
0 3410
 
1.3%
-127 3275
 
1.2%
145 3233
 
1.2%
-45 2884
 
1.1%
Other values (350) 234962
86.4%
ValueCountFrequency (%)
-179 131
 
< 0.1%
-178 535
0.2%
-177 1190
0.4%
-176 1002
0.4%
-175 252
 
0.1%
-174 560
0.2%
-173 1137
0.4%
-172 598
0.2%
-171 109
 
< 0.1%
-170 673
0.2%
ValueCountFrequency (%)
180 5053
1.9%
179 279
 
0.1%
178 1086
 
0.4%
177 1775
 
0.7%
176 1447
 
0.5%
175 471
 
0.2%
174 1148
 
0.4%
173 1504
 
0.6%
172 944
 
0.3%
171 335
 
0.1%

Activities
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
900 trining working 1930 training
46122 
dream
41963 
training
24497 
New folder
24370 
Emo whole day sales and training
20659 
Other values (22)
114187 

Length

Max length44
Median length25
Mean length18.097197
Min length4

Characters and Unicode

Total characters4918782
Distinct characters53
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row900 trining working 1930 training
2nd row900 trining working 1930 training
3rd row900 trining working 1930 training
4th row900 trining working 1930 training
5th row900 trining working 1930 training

Common Values

ValueCountFrequency (%)
900 trining working 1930 training 46122
17.0%
dream 41963
15.4%
training 24497
9.0%
New folder 24370
9.0%
Emo whole day sales and training 20659
 
7.6%
work 16149
 
5.9%
walking meeting 11475
 
4.2%
motivirashta presentacia pred ekipa i rabota 10673
 
3.9%
training machines 9630
 
3.5%
training with Deto 8737
 
3.2%
Other values (17) 57523
21.2%

Length

2023-07-16T17:53:03.626661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
training 119948
 
14.9%
900 46122
 
5.7%
working 46122
 
5.7%
1930 46122
 
5.7%
trining 46122
 
5.7%
dream 41963
 
5.2%
sales 31595
 
3.9%
emo 29596
 
3.7%
day 26272
 
3.3%
new 24370
 
3.0%
Other values (52) 347905
43.2%

Most occurring characters

ValueCountFrequency (%)
534339
 
10.9%
i 520519
 
10.6%
n 499446
 
10.2%
a 390341
 
7.9%
r 358093
 
7.3%
t 298520
 
6.1%
e 297573
 
6.0%
g 254171
 
5.2%
o 210415
 
4.3%
0 162978
 
3.3%
Other values (43) 1392387
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3923669
79.8%
Space Separator 534339
 
10.9%
Decimal Number 355670
 
7.2%
Uppercase Letter 105104
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 520519
13.3%
n 499446
12.7%
a 390341
9.9%
r 358093
9.1%
t 298520
 
7.6%
e 297573
 
7.6%
g 254171
 
6.5%
o 210415
 
5.4%
w 148104
 
3.8%
d 138236
 
3.5%
Other values (24) 808251
20.6%
Uppercase Letter
ValueCountFrequency (%)
E 30020
28.6%
N 24370
23.2%
D 16941
16.1%
Y 8204
 
7.8%
C 5248
 
5.0%
V 4390
 
4.2%
B 3265
 
3.1%
И 3067
 
2.9%
С 3067
 
2.9%
S 2624
 
2.5%
Other values (3) 3908
 
3.7%
Decimal Number
ValueCountFrequency (%)
0 162978
45.8%
9 92244
25.9%
3 46122
 
13.0%
1 46122
 
13.0%
2 8204
 
2.3%
Space Separator
ValueCountFrequency (%)
534339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3939830
80.1%
Common 890009
 
18.1%
Cyrillic 88943
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 520519
13.2%
n 499446
12.7%
a 390341
9.9%
r 358093
9.1%
t 298520
 
7.6%
e 297573
 
7.6%
g 254171
 
6.5%
o 210415
 
5.3%
w 148104
 
3.8%
d 138236
 
3.5%
Other values (24) 824412
20.9%
Cyrillic
ValueCountFrequency (%)
о 12268
13.8%
н 12268
13.8%
е 12268
13.8%
с 9201
10.3%
в 6134
6.9%
т 6134
6.9%
а 6134
6.9%
р 6134
6.9%
м 6134
6.9%
з 3067
 
3.4%
Other values (3) 9201
10.3%
Common
ValueCountFrequency (%)
534339
60.0%
0 162978
 
18.3%
9 92244
 
10.4%
3 46122
 
5.2%
1 46122
 
5.2%
2 8204
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4829839
98.2%
Cyrillic 88943
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
534339
11.1%
i 520519
 
10.8%
n 499446
 
10.3%
a 390341
 
8.1%
r 358093
 
7.4%
t 298520
 
6.2%
e 297573
 
6.2%
g 254171
 
5.3%
o 210415
 
4.4%
0 162978
 
3.4%
Other values (30) 1303444
27.0%
Cyrillic
ValueCountFrequency (%)
о 12268
13.8%
н 12268
13.8%
е 12268
13.8%
с 9201
10.3%
в 6134
6.9%
т 6134
6.9%
а 6134
6.9%
р 6134
6.9%
м 6134
6.9%
з 3067
 
3.4%
Other values (3) 9201
10.3%
Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
office work
72507 
sport
69998 
dream
66333 
public speaking
10673 
sales
8434 
Other values (16)
43853 

Length

Max length19
Median length5
Mean length8.1438863
Min length5

Characters and Unicode

Total characters2213492
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsport
2nd rowsport
3rd rowsport
4th rowsport
5th rowsport

Common Values

ValueCountFrequency (%)
office work 72507
26.7%
sport 69998
25.8%
dream 66333
24.4%
public speaking 10673
 
3.9%
sales 8434
 
3.1%
negotiation 7612
 
2.8%
creative writing 4927
 
1.8%
training 4357
 
1.6%
phone calls 4154
 
1.5%
competition 3748
 
1.4%
Other values (11) 19055
 
7.0%

Length

2023-07-16T17:53:03.740753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
work 72508
19.1%
office 72507
19.1%
sport 69998
18.4%
dream 66333
17.4%
public 10673
 
2.8%
speaking 10673
 
2.8%
sales 8434
 
2.2%
negotiation 7612
 
2.0%
writing 5485
 
1.4%
creative 4927
 
1.3%
Other values (21) 51369
13.5%

Most occurring characters

ValueCountFrequency (%)
o 264379
11.9%
r 236020
 
10.7%
e 208832
 
9.4%
i 168279
 
7.6%
f 145014
 
6.6%
t 129801
 
5.9%
a 121308
 
5.5%
p 109103
 
4.9%
108721
 
4.9%
s 107470
 
4.9%
Other values (17) 614565
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2103311
95.0%
Space Separator 108721
 
4.9%
Uppercase Letter 1460
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 264379
12.6%
r 236020
11.2%
e 208832
9.9%
i 168279
 
8.0%
f 145014
 
6.9%
t 129801
 
6.2%
a 121308
 
5.8%
p 109103
 
5.2%
s 107470
 
5.1%
c 101700
 
4.8%
Other values (12) 511405
24.3%
Uppercase Letter
ValueCountFrequency (%)
P 642
44.0%
Q 642
44.0%
C 88
 
6.0%
B 88
 
6.0%
Space Separator
ValueCountFrequency (%)
108721
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2104771
95.1%
Common 108721
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 264379
12.6%
r 236020
11.2%
e 208832
9.9%
i 168279
 
8.0%
f 145014
 
6.9%
t 129801
 
6.2%
a 121308
 
5.8%
p 109103
 
5.2%
s 107470
 
5.1%
c 101700
 
4.8%
Other values (16) 512865
24.4%
Common
ValueCountFrequency (%)
108721
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2213492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 264379
11.9%
r 236020
 
10.7%
e 208832
 
9.4%
i 168279
 
7.6%
f 145014
 
6.6%
t 129801
 
5.9%
a 121308
 
5.5%
p 109103
 
4.9%
108721
 
4.9%
s 107470
 
4.9%
Other values (17) 614565
27.8%

Controled stress
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0.0
184656 
1.0
87142 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters815394
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 184656
67.9%
1.0 87142
32.1%

Length

2023-07-16T17:53:03.844754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:03.919910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 184656
67.9%
1.0 87142
32.1%

Most occurring characters

ValueCountFrequency (%)
0 456454
56.0%
. 271798
33.3%
1 87142
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 543596
66.7%
Other Punctuation 271798
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 456454
84.0%
1 87142
 
16.0%
Other Punctuation
ValueCountFrequency (%)
. 271798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 815394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 456454
56.0%
. 271798
33.3%
1 87142
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 815394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 456454
56.0%
. 271798
33.3%
1 87142
 
10.7%

stress
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0.0
236596 
1.0
35202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters815394
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 236596
87.0%
1.0 35202
 
13.0%

Length

2023-07-16T17:53:04.000396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:04.080742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 236596
87.0%
1.0 35202
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 508394
62.3%
. 271798
33.3%
1 35202
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 543596
66.7%
Other Punctuation 271798
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 508394
93.5%
1 35202
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 271798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 815394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 508394
62.3%
. 271798
33.3%
1 35202
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 815394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 508394
62.3%
. 271798
33.3%
1 35202
 
4.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0.0
270578 
1.0
 
1220

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters815394
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 270578
99.6%
1.0 1220
 
0.4%

Length

2023-07-16T17:53:04.165331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:04.244331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 270578
99.6%
1.0 1220
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 542376
66.5%
. 271798
33.3%
1 1220
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 543596
66.7%
Other Punctuation 271798
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 542376
99.8%
1 1220
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 271798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 815394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 542376
66.5%
. 271798
33.3%
1 1220
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 815394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 542376
66.5%
. 271798
33.3%
1 1220
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0.0
203295 
1.0
68503 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters815394
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 203295
74.8%
1.0 68503
 
25.2%

Length

2023-07-16T17:53:04.323335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:04.402012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 203295
74.8%
1.0 68503
 
25.2%

Most occurring characters

ValueCountFrequency (%)
0 475093
58.3%
. 271798
33.3%
1 68503
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 543596
66.7%
Other Punctuation 271798
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 475093
87.4%
1 68503
 
12.6%
Other Punctuation
ValueCountFrequency (%)
. 271798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 815394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 475093
58.3%
. 271798
33.3%
1 68503
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 815394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 475093
58.3%
. 271798
33.3%
1 68503
 
8.4%

Name of the volunteer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
EM
263739 
NP
 
4152
AB
 
3265
VM
 
642

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters543596
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEM
2nd rowEM
3rd rowEM
4th rowEM
5th rowEM

Common Values

ValueCountFrequency (%)
EM 263739
97.0%
NP 4152
 
1.5%
AB 3265
 
1.2%
VM 642
 
0.2%

Length

2023-07-16T17:53:04.485827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:53:04.674759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
em 263739
97.0%
np 4152
 
1.5%
ab 3265
 
1.2%
vm 642
 
0.2%

Most occurring characters

ValueCountFrequency (%)
M 264381
48.6%
E 263739
48.5%
N 4152
 
0.8%
P 4152
 
0.8%
A 3265
 
0.6%
B 3265
 
0.6%
V 642
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 543596
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 264381
48.6%
E 263739
48.5%
N 4152
 
0.8%
P 4152
 
0.8%
A 3265
 
0.6%
B 3265
 
0.6%
V 642
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 543596
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 264381
48.6%
E 263739
48.5%
N 4152
 
0.8%
P 4152
 
0.8%
A 3265
 
0.6%
B 3265
 
0.6%
V 642
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 543596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 264381
48.6%
E 263739
48.5%
N 4152
 
0.8%
P 4152
 
0.8%
A 3265
 
0.6%
B 3265
 
0.6%
V 642
 
0.1%

Interactions

2023-07-16T17:52:53.334832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:11.582021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.333692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.774189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.061345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.388275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.864238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.119782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:28.410612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.858542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.057350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.288593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.411787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.735221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.986732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:44.355733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.613663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.812927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.130784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:53.444948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:11.719022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.451693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.883195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.176238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.510185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.966159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.225622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:28.645621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.960537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.160407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.394463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.518368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.857734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.091732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:44.464732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.722133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.918928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.240775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:53.560569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:11.910028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.591694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.008181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.318237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.640178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.088162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.350622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:28.791612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.075619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.271547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.509099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.641173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.975113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.207731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:44.589732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.844139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.144261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.364866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:53.672779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:12.093023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.722692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.119821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.442238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.755177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.200160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.466623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:28.914612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.186619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.381845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.615853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.754707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.088777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.315313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:44.708732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.955134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.255260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.474858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:53.893799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:12.238021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.869693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.231826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.576245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.887177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.318160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.585612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.030786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.304619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.492847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.724954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.875705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.205776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.430800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:44.832740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.063133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.370003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.591866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.005455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:12.380023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.997704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.346827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.693245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.026178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.432210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.703620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.152856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.422359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.604388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.834553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.990705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.318777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.547457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:44.950995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.179134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.494003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.710066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.117946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:12.519032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:15.118692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.457315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.814245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.147178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.543210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.820620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.268324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.540106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.711388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.943553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.109163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.437869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.663743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.064425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.294043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.610003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.820571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.229190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:12.804022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:15.243701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.570856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:19.940237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.282182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.661324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:26.932621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.384324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.657108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:33.926195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.052280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.228119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.552871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.778740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.184504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.409042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.722003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.934916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.348191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:12.947021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:15.368704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.690984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.085238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.412178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.788327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.060620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.509324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.778105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.041242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.170271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.354613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.677298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:42.897732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.304554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.530056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.842004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.053192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.462288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.119023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:15.499693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:17.806983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.205236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.532177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:24.903323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.181620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.638321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.889489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.147293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.291279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.465613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.795357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.012731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.423554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.649736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:49.958540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.171934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.573288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.252022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:15.633693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.025976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.320958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.650177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.023327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.295612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.753320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:31.995343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.254331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.395766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.579613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:40.909365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.131732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.535554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.760522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.070735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.284934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.679290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.372020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:15.758692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.133983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.431143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.763178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.139324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.403621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.867321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.112643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.359364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.499447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.684615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.017310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.241732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.643554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.874693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.178293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.387934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.799290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.501022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:15.896693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.248263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.554351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:22.893150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.265316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.526612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:29.987320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.234021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.471237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.614866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.804616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.140431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.363732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.764555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:47.989693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.301387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.507936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:54.917238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.622603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.032693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.364096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.674176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.024145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.389324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.659620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.120313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.353021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.590188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.729454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:38.925613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.259431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.491741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.887554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.111126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.423503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.628943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:55.030048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.740640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.156695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.481090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.791177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.142154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.512316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.790621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.245313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.470350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.706188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.842523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.041615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.380432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.615742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:45.999616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.223126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.542784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.748266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:55.148048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.862694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.281727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.601094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:20.916860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.382864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.638324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:27.914611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.372320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.589356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.829180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:36.957523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.268567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.502540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.741732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.127561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.343599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.660779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.875266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:55.268049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:13.984694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.408900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.717667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.037866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.503856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.757323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:28.042611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.494097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.706350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:34.948188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.068500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.385585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.624075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.870740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.258563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.462603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.783678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:52.990968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:55.384048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.102693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.534901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.836675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.157866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.625159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.877323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:28.169612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.615097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.831344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.063189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.188787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.503585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.748077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:43.994732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.381561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.581633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:50.894677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:53.105879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:55.500215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:14.218693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:16.657403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:18.950344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:21.275273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:23.747182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:25.992324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:28.290612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:30.736188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:32.947351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:35.174592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:37.301787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:39.620593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:41.872195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:44.118741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:46.495664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:48.698927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:51.007678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T17:52:53.219832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-16T17:53:04.762501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HourHRBRPostureActivityPeakAccelerationBRAmplitudeECGAmplitudeECGNoiseHRConfidenceHRVCoreTempImpulseLoadWalkStepsRunStepsBoundsMinorImpactsPeakAccelPhipeakAccelThetaYearMonthWeekdayDateMajorImpactsActivitiesActivities DetailedControled stressstressBefore Controled stressAfter controlled stressName of the volunteer
Hour1.0000.3910.1160.1950.2190.2160.0850.3390.328-0.0610.0130.4420.2540.3030.1470.379-0.2650.4180.0510.4040.3680.4130.5210.2900.5830.5240.7330.3860.1240.8950.258
HR0.3911.000-0.0300.2840.5690.5210.3580.3760.718-0.273-0.3520.7900.2180.2790.0240.214-0.3620.500-0.0710.3410.2630.2470.3310.0520.3550.3260.2460.2750.0570.7260.112
BR0.116-0.0301.000-0.037-0.089-0.073-0.123-0.045-0.1000.0410.0700.0150.0110.0150.0370.100-0.039-0.0760.0570.1570.1710.1610.2020.0290.2310.2130.2930.1520.0840.4020.100
Posture0.1950.284-0.0371.0000.1350.1330.1810.2940.341-0.0880.0830.2670.0540.0730.0450.047-0.1480.351-0.2980.2970.4050.3930.5050.0720.5180.5040.4440.3800.0490.9090.580
Activity0.2190.569-0.0890.1351.0000.8330.3200.2220.595-0.264-0.0710.3790.2020.2430.0820.253-0.1890.332-0.1210.0490.0540.0600.0810.0440.1090.0880.0500.0610.0000.1640.026
PeakAcceleration0.2160.521-0.0730.1330.8331.0000.2910.2090.543-0.238-0.0680.3510.1980.2360.0850.245-0.1800.315-0.1370.0430.0180.0140.0390.0220.0430.0360.0220.0030.0040.0310.007
BRAmplitude0.0850.358-0.1230.1810.3200.2911.0000.0370.419-0.364-0.0240.2130.1730.213-0.0760.085-0.0810.190-0.1330.2290.1150.0610.1630.0710.1640.1260.0930.0460.0360.1290.096
ECGAmplitude0.3390.376-0.0450.2940.2220.2090.0371.0000.4540.1180.0200.4710.1230.2240.0110.277-0.4350.433-0.1080.1980.2340.2290.3600.0540.3850.4140.2900.0620.2170.4200.255
ECGNoise0.3280.718-0.1000.3410.5950.5430.4190.4541.000-0.423-0.1290.6110.1600.250-0.1090.169-0.3680.489-0.1420.0940.0430.0400.0570.0230.0570.0550.0250.0310.0580.0190.037
HRConfidence-0.061-0.2730.041-0.088-0.264-0.238-0.3640.118-0.4231.0000.012-0.174-0.087-0.0880.1710.0280.075-0.0880.0430.1970.1280.0920.1370.0270.1390.1160.0120.0270.1830.0900.084
HRV0.013-0.3520.0700.083-0.071-0.068-0.0240.020-0.1290.0121.000-0.364-0.112-0.1080.021-0.0050.158-0.075-0.0530.2860.2050.1920.2550.1710.2760.2560.4780.2590.0650.2740.110
CoreTemp0.4420.7900.0150.2670.3790.3510.2130.4710.611-0.174-0.3641.0000.2370.3030.0240.239-0.3830.541-0.0290.3840.4060.3060.4580.1250.4940.4500.4390.3700.0810.8610.132
ImpulseLoad0.2540.2180.0110.0540.2020.1980.1730.1230.160-0.087-0.1120.2371.0000.9620.6320.634-0.0460.254-0.1170.3100.3060.2320.3930.2570.4170.3480.3100.1990.2450.3280.062
WalkSteps0.3030.2790.0150.0730.2430.2360.2130.2240.250-0.088-0.1080.3030.9621.0000.5620.648-0.2130.378-0.1060.2890.2970.2390.3690.1740.3890.3020.2800.2080.1360.3420.065
RunSteps0.1470.0240.0370.0450.0820.085-0.0760.011-0.1090.1710.0210.0240.6320.5621.0000.6010.3170.050-0.0910.1800.2820.2470.4100.4880.4730.2590.3860.1840.0320.2480.048
Bounds0.3790.2140.1000.0470.2530.2450.0850.2770.1690.028-0.0050.2390.6340.6480.6011.000-0.1900.296-0.0370.1760.2010.2510.4540.6860.5130.2140.4060.1360.0230.2050.035
MinorImpacts-0.265-0.362-0.039-0.148-0.189-0.180-0.081-0.435-0.3680.0750.158-0.383-0.046-0.2130.317-0.1901.000-0.452-0.0560.1780.3940.3920.6890.6190.6850.5850.4900.3000.0490.7930.700
PeakAccelPhi0.4180.500-0.0760.3510.3320.3150.1900.4330.489-0.088-0.0750.5410.2540.3780.0500.296-0.4521.000-0.1660.2880.3780.3950.4840.0880.5080.4930.3730.4170.0470.8960.578
peakAccelTheta0.051-0.0710.057-0.298-0.121-0.137-0.133-0.108-0.1420.043-0.053-0.029-0.117-0.106-0.091-0.037-0.056-0.1661.0000.1420.1640.2160.2770.0500.2950.2590.2990.1900.0580.4790.169
Year0.4040.3410.1570.2970.0490.0430.2290.1980.0940.1970.2860.3840.3100.2890.1800.1760.1780.2880.1421.0000.6110.5581.0000.0861.0000.9030.1520.4170.1050.3450.193
Month0.3680.2630.1710.4050.0540.0180.1150.2340.0430.1280.2050.4060.3060.2970.2820.2010.3940.3780.1640.6111.0000.4841.0000.1470.9700.7530.2020.6660.3480.3640.381
Weekday0.4130.2470.1610.3930.0600.0140.0610.2290.0400.0920.1920.3060.2320.2390.2470.2510.3920.3950.2160.5580.4841.0001.0000.2380.9060.7190.2280.4540.3070.6120.482
Date0.5210.3310.2020.5050.0810.0390.1630.3600.0570.1370.2550.4580.3930.3690.4100.4540.6890.4840.2771.0001.0001.0001.0000.7080.9050.7630.5860.8930.4620.8281.000
MajorImpacts0.2900.0520.0290.0720.0440.0220.0710.0540.0230.0270.1710.1250.2570.1740.4880.6860.6190.0880.0500.0860.1470.2380.7081.0000.7070.2190.2650.0700.0120.1060.022
Activities0.5830.3550.2310.5180.1090.0430.1640.3850.0570.1390.2760.4940.4170.3890.4730.5130.6850.5080.2951.0000.9700.9060.9050.7071.0000.8830.8671.0000.4620.9851.000
Activities Detailed0.5240.3260.2130.5040.0880.0360.1260.4140.0550.1160.2560.4500.3480.3020.2590.2140.5850.4930.2590.9030.7530.7190.7630.2190.8831.0001.0001.0000.1110.9971.000
Controled stress0.7330.2460.2930.4440.0500.0220.0930.2900.0250.0120.4780.4390.3100.2800.3860.4060.4900.3730.2990.1520.2020.2280.5860.2650.8671.0001.0000.2650.0460.3990.194
stress0.3860.2750.1520.3800.0610.0030.0460.0620.0310.0270.2590.3700.1990.2080.1840.1360.3000.4170.1900.4170.6660.4540.8930.0701.0001.0000.2651.0000.0260.2240.326
Before Controled stress0.1240.0570.0840.0490.0000.0040.0360.2170.0580.1830.0650.0810.2450.1360.0320.0230.0490.0470.0580.1050.3480.3070.4620.0120.4620.1110.0460.0261.0000.0390.011
After controlled stress0.8950.7260.4020.9090.1640.0310.1290.4200.0190.0900.2740.8610.3280.3420.2480.2050.7930.8960.4790.3450.3640.6120.8280.1060.9850.9970.3990.2240.0391.0000.101
Name of the volunteer0.2580.1120.1000.5800.0260.0070.0960.2550.0370.0840.1100.1320.0620.0650.0480.0350.7000.5780.1690.1930.3810.4821.0000.0221.0001.0000.1940.3260.0110.1011.000

Missing values

2023-07-16T17:52:55.715124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T17:52:56.398065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimeYearMonthWeekdayHourHRBRPostureActivityPeakAccelerationBRAmplitudeECGAmplitudeECGNoiseHRConfidenceHRVCoreTempImpulseLoadWalkStepsRunStepsBoundsJumpsMinorImpactsDateMajorImpactsAvForceDevRateAvStepImpulseAvStepPeriodJumpFlightTimePeakAccelPhipeakAccelThetaActivitiesActivities DetailedControled stressstressBefore Controled stressAfter controlled stressName of the volunteer
026.3.2020 9:02:282020MarchThursday97915160.010.0225930.002380.000101004537.111738100026.3.202000000163-50900 trining working 1930 trainingsport1.00.00.00.0EM
126.3.2020 9:02:292020MarchThursday97915160.010.1021630.002340.000101004537.111738100026.3.202000000168-87900 trining working 1930 trainingsport1.00.00.00.0EM
226.3.2020 9:02:302020MarchThursday97915170.010.0319430.002340.000101004537.111738100026.3.202000000164-47900 trining working 1930 trainingsport1.00.00.00.0EM
326.3.2020 9:02:312020MarchThursday97915170.010.0220380.002320.000101004537.111738100026.3.202000000163-43900 trining working 1930 trainingsport1.00.00.00.0EM
426.3.2020 9:02:322020MarchThursday97915170.000.0217700.002320.000101004537.111738100026.3.202000000163-50900 trining working 1930 trainingsport1.00.00.00.0EM
526.3.2020 9:02:332020MarchThursday97815170.010.0217060.002340.000101004637.111738100026.3.202000000165-43900 trining working 1930 trainingsport1.00.00.00.0EM
626.3.2020 9:02:342020MarchThursday97715150.010.0217670.002340.000101004637.111738100026.3.202000000163-50900 trining working 1930 trainingsport1.00.00.00.0EM
726.3.2020 9:02:352020MarchThursday97715150.010.0316390.002300.000081004637.111738100026.3.202000000166-41900 trining working 1930 trainingsport1.00.00.00.0EM
826.3.2020 9:02:362020MarchThursday97815150.010.0315780.002300.000081004637.111738100026.3.202000000163-52900 trining working 1930 trainingsport1.00.00.00.0EM
926.3.2020 9:02:372020MarchThursday97915150.010.0216290.002360.000101004637.111738100026.3.202000000165-45900 trining working 1930 trainingsport1.00.00.00.0EM
TimeYearMonthWeekdayHourHRBRPostureActivityPeakAccelerationBRAmplitudeECGAmplitudeECGNoiseHRConfidenceHRVCoreTempImpulseLoadWalkStepsRunStepsBoundsJumpsMinorImpactsDateMajorImpactsAvForceDevRateAvStepImpulseAvStepPeriodJumpFlightTimePeakAccelPhipeakAccelThetaActivitiesActivities DetailedControled stressstressBefore Controled stressAfter controlled stressName of the volunteer
27178822.6.2022 6:35:052022JuneWednesday6862130.160.55231230.003500.00084993736.910730100122.6.202200,442,90,368015380Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27178922.6.2022 6:35:062022JuneWednesday68621110.340.42292980.003500.00172903736.910931100122.6.202200,592,750,351015173Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179022.6.2022 6:35:072022JuneWednesday68521220.310.37269460.003560.00152833736.910931100122.6.202200,592,750,351014391Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179122.6.2022 6:35:082022JuneWednesday68420290.180.22228070.003560.00108723736.910931100122.6.202200,592,750,351014191Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179222.6.2022 6:35:092022JuneWednesday68420-330.100.14221190.003520.00108633836.910931100122.6.202200,592,750,351014095Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179322.6.2022 6:35:102022JuneWednesday68420-340.040.08315640.003520.00098563836.910931100122.6.202200,592,750,351014091Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179422.6.2022 6:35:112022JuneWednesday68420-350.010.03398140.003520.00070523936.910931100122.6.20220000014291Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179522.6.2022 6:35:122022JuneWednesday68419-360.040.19463790.003520.00100463936.910931100122.6.20220000013395Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179622.6.2022 6:35:132022JuneWednesday68519-380.050.09491540.003440.00264434036.910931100122.6.20220000013595Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM
27179722.6.2022 6:35:142022JuneWednesday68519-400.060.10408310.003440.00254384036.910931100122.6.20220000013499Измерване на стрес нивото Соменсоcognitive workout0.01.00.00.0EM